Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each instance, which can lead to the leakage of sensitive information (such as smoking, diseases, etc.) about the instances. However, existing MLL suffer from failures in protection for sensitive information. In this paper, we propose a novel setting named Multi-Label Learning from Privacy-Label (MLLPL), which Concealing Labels via Privacy-Label Unit (CLPLU). Specifically, during the labeling phase, each privacy-label is randomly combined with a non-privacy label to form a Privacy-Label Unit (PLU). If any label within a PLU is positive, the unit is labeled as positive; otherwise, it is labeled negative, as shown in Figure 1. PLU ensures that only non-privacy labels are appear in the label set, while the privacy-labels remain concealed. Moreover, we further propose a Privacy-Label Unit Loss (PLUL) to learn the optimal classifier by minimizing the empirical risk of PLU. Experimental results on multiple benchmark datasets demonstrate the effectiveness and superiority of the proposed method.

翻译：多标签学习（MLL）通常涉及为每个实例分配多个相关标签，这可能导致实例敏感信息（如吸烟、疾病等）的泄露。然而，现有的多标签学习在保护敏感信息方面存在不足。本文提出了一种名为"基于隐私标签的多标签学习"（MLLPL）的新框架，通过隐私标签单元（CLPLU）实现标签隐藏。具体而言，在标注阶段，每个隐私标签会随机与非隐私标签组合形成隐私标签单元（PLU）。若PLU中任意标签为阳性，则该单元标记为阳性；否则标记为阴性，如图1所示。PLU确保标签集中仅出现非隐私标签，而隐私标签始终保持隐藏。此外，我们进一步提出隐私标签单元损失函数（PLUL），通过最小化PLU的经验风险来学习最优分类器。在多个基准数据集上的实验结果证明了所提方法的有效性和优越性。